This short article describes the main new features added to SLEPc, the Scalable Library for Eigenvalue Problem Computations, in the past two and a half years, corresponding to five release versions. The main novelty is the extension of the SVD module with new problem types, such as the generalized SVD or the hyperbolic SVD. Additionally, many improvements have been incorporated in different parts of the library, including contour integral eigensolvers, preconditioning, and GPU support.

This paper presents a general energy management system for HPC clusters and cloud infrastructures that powers off cluster nodes when they are not being used, and conversely powers them on when they are needed. This system can be integrated with different HPC cluster middleware, such as Batch-Queuing Systems or Cloud Management Systems, by using a set of connectors, and is also able to deal with different mechanisms for powering on and off the computing nodes (such as Wake-on-Lan, Power Device Units, Intelligent Platform Management Interface or other infrastructure-specific mechanisms). While some existing Batch-Queuing Systems provide energy saving mechanisms, other popular choices lack this feature. Cloud management middleware do not generally provide this feature out of the box, and incorporating it implies making modifications to the middleware. The advantage of our approach is that it can be integrated with different resource management middleware, without needing any modification of that middleware. The paper describes the successful integration of the system proposed with the popular Torque/PBS management system, and also with the OpenNebula open source cloud management tool. Two real use-cases are presented, involving two different HPC clusters. These use cases show significant energy/costs savings of 38% and 16%.

With the advent of cloud technologies the scientists have access to different cloud infrastructures in order to deploy all the virtual machines they need to perform the computations required in their research works. This paper describes a software architecture and a description language to simplify the creation of all the needed resources, and the elastic evolution of the computing infrastructure depending on the application requirements and some QoS features.

Diesel engines are fuel efficient which benefits the reduction of CO2released to the atmosphere compared with gasoline engines, but still result in negative environmental impact related to their emissions. As new degrees of freedom are created, due to advances in technology, the complicated processes of emission formation are difficult to assess. This paper studies the feasibility of using artificial neural networks (ANNs) in combination with genetic algorithms (GAs) to optimize the diesel engine settings. The objective of the optimization was to find settings that complied with the increasingly stringent emission regulations while also maintaining, or even reducing the fuel consumption. A large database of stationary engine tests, covering a wide range of experimental conditions was used for this analysis. The ANNs were used as a simulation tool, receiving as inputs the engine operating parameters, and producing as outputs the resulting emission levels and fuel consumption. The ANN outputs were then used to evaluate the objective function of the optimization process, which was performed with a GA approach. The combination of ANN and GA for the optimization of two different engine operating conditions was analyzed and important reductions in emissions and fuel consumption were reached, while also keeping the computational times low